Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory88.3 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:50:22.638210
Analysis finished2020-08-25 00:50:40.867080
Duration18.23 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz5 is highly correlated with oz3High correlation
oz3 is highly correlated with oz5High correlation
oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.765476427972316e-10
Minimum-2.2574944496154785
Maximum2.333986520767212
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:40.912284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.25749445
5-th percentile-1.664599144
Q1-0.78919743
median0.04062976688
Q30.7066226155
95-th percentile1.623239952
Maximum2.333986521
Range4.59148097
Interquartile range (IQR)1.495820045

Descriptive statistics

Standard deviation0.9999999964
Coefficient of variation (CV)-1478092500
Kurtosis-0.6757250358
Mean-6.765476428e-10
Median Absolute Deviation (MAD)0.7391045019
Skewness-0.05112453848
Sum-3.382738214e-07
Variance0.9999999928
2020-08-25T00:50:41.006042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.67968434110.2%
 
0.118204213710.2%
 
1.24345552910.2%
 
0.61454433210.2%
 
-0.29799923310.2%
 
0.0880500748810.2%
 
1.23692965510.2%
 
-1.40092098710.2%
 
0.507140278810.2%
 
-2.2574944510.2%
 
1.35022425710.2%
 
0.572580158710.2%
 
-0.434728205210.2%
 
0.665358126210.2%
 
1.44204664210.2%
 
2.00909590710.2%
 
0.524766445210.2%
 
0.466842204310.2%
 
0.952451348310.2%
 
-0.0642500668810.2%
 
1.28776955610.2%
 
-1.07683777810.2%
 
0.709627628310.2%
 
-1.42280948210.2%
 
0.505217850210.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.2574944510.2%
 
-2.20732641210.2%
 
-2.09211111110.2%
 
-2.08289647110.2%
 
-2.07150077810.2%
 
-2.0445170410.2%
 
-2.01788854610.2%
 
-2.00860166510.2%
 
-2.0083341610.2%
 
-1.98517954310.2%
 
ValueCountFrequency (%) 
2.33398652110.2%
 
2.23807239510.2%
 
2.15891361210.2%
 
2.13331174910.2%
 
2.12914490710.2%
 
2.09507226910.2%
 
2.00909590710.2%
 
2.008325110.2%
 
1.94578456910.2%
 
1.92142748810.2%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.939540177583695e-10
Minimum-1.7384207248687744
Maximum1.6432623863220217
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:41.106886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.738420725
5-th percentile-1.595411408
Q1-0.8511608839
median0.004745978105
Q30.8631366044
95-th percentile1.49386189
Maximum1.643262386
Range3.381683111
Interquartile range (IQR)1.714297488

Descriptive statistics

Standard deviation0.9999999972
Coefficient of variation (CV)1006082756
Kurtosis-1.207661238
Mean9.939540178e-10
Median Absolute Deviation (MAD)0.8570299149
Skewness-0.06681402082
Sum4.969770089e-07
Variance0.9999999945
2020-08-25T00:50:41.206874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.395505964810.2%
 
0.796250343310.2%
 
0.465142726910.2%
 
-0.464517027110.2%
 
-1.45132780110.2%
 
-0.372722417110.2%
 
-0.981776833510.2%
 
-0.593107998410.2%
 
0.531589329210.2%
 
1.53974485410.2%
 
0.427416592810.2%
 
1.23317968810.2%
 
1.64326238610.2%
 
0.254567086710.2%
 
-1.13067305110.2%
 
0.359722495110.2%
 
-1.68373513210.2%
 
0.216951385110.2%
 
-1.13744461510.2%
 
-1.00902414310.2%
 
0.829473137910.2%
 
0.334171086510.2%
 
-1.63597047310.2%
 
0.983777821110.2%
 
-1.27615392210.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.73842072510.2%
 
-1.73702824110.2%
 
-1.7297707810.2%
 
-1.71776080110.2%
 
-1.71461200710.2%
 
-1.71434652810.2%
 
-1.71000003810.2%
 
-1.70747065510.2%
 
-1.69736814510.2%
 
-1.68549549610.2%
 
ValueCountFrequency (%) 
1.64326238610.2%
 
1.64007210710.2%
 
1.63826525210.2%
 
1.61315679610.2%
 
1.61163365810.2%
 
1.6076141610.2%
 
1.60446059710.2%
 
1.59923756110.2%
 
1.59597456510.2%
 
1.59437215310.2%
 

oz3
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.759058356285095e-10
Minimum-2.041592836380005
Maximum3.0931398868560787
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:41.319915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.041592836
5-th percentile-1.325843465
Q1-0.7353193462
median-0.2076342776
Q30.6148765534
95-th percentile1.891593409
Maximum3.093139887
Range5.134732723
Interquartile range (IQR)1.3501959

Descriptive statistics

Standard deviation0.9999999989
Coefficient of variation (CV)-2101256013
Kurtosis-0.05884190209
Mean-4.759058356e-10
Median Absolute Deviation (MAD)0.6436958835
Skewness0.6847154757
Sum-2.379529178e-07
Variance0.9999999977
2020-08-25T00:50:41.417024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.439452379910.2%
 
-1.05924713610.2%
 
-0.730770349510.2%
 
0.0725951567310.2%
 
-0.307953000110.2%
 
-0.761047899710.2%
 
-1.11975848710.2%
 
2.13405561410.2%
 
0.0734031647410.2%
 
1.32163906110.2%
 
-1.06508517310.2%
 
-2.00908088710.2%
 
2.63799524310.2%
 
-0.38004374510.2%
 
1.06867814110.2%
 
0.278644323310.2%
 
-0.890981793410.2%
 
2.29038572310.2%
 
-0.377763688610.2%
 
-1.20965290110.2%
 
-0.477370411210.2%
 
-1.19403612610.2%
 
-0.37761074310.2%
 
-0.319395959410.2%
 
-0.382008582410.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.04159283610.2%
 
-2.00908088710.2%
 
-2.00649189910.2%
 
-1.65927934610.2%
 
-1.63305544910.2%
 
-1.60504639110.2%
 
-1.57688415110.2%
 
-1.55085682910.2%
 
-1.54854941410.2%
 
-1.53826355910.2%
 
ValueCountFrequency (%) 
3.09313988710.2%
 
3.03357958810.2%
 
3.00588178610.2%
 
2.91045188910.2%
 
2.80024170910.2%
 
2.63799524310.2%
 
2.41751170210.2%
 
2.39339494710.2%
 
2.29038572310.2%
 
2.28427171710.2%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.622890502214432e-10
Minimum-1.7375524044036863
Maximum1.8207112550735476
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:41.530571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.737552404
5-th percentile-1.581777894
Q1-0.8686255813
median0.007074554451
Q30.7598236501
95-th percentile1.614264351
Maximum1.820711255
Range3.558263659
Interquartile range (IQR)1.628449231

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)-1039188796
Kurtosis-1.10033482
Mean-9.622890502e-10
Median Absolute Deviation (MAD)0.8248062134
Skewness0.02400012278
Sum-4.811445251e-07
Variance0.9999999998
2020-08-25T00:50:41.636471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.66210818310.2%
 
-0.474465727810.2%
 
1.31508684210.2%
 
-1.73305952510.2%
 
-0.690749406810.2%
 
1.76858055610.2%
 
-1.40445244310.2%
 
0.980793476110.2%
 
1.16080999410.2%
 
1.50456070910.2%
 
1.36979258110.2%
 
-0.538414955110.2%
 
-0.991621494310.2%
 
-0.281088322410.2%
 
-0.276049435110.2%
 
0.269703686210.2%
 
0.415877014410.2%
 
1.37959885610.2%
 
-1.26701676810.2%
 
0.286633849110.2%
 
1.70792472410.2%
 
0.588242530810.2%
 
0.473321706110.2%
 
1.12813162810.2%
 
-1.29083323510.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.73755240410.2%
 
-1.73305952510.2%
 
-1.73096275310.2%
 
-1.71996080910.2%
 
-1.70740842810.2%
 
-1.70469415210.2%
 
-1.70122301610.2%
 
-1.68330335610.2%
 
-1.67890191110.2%
 
-1.67830228810.2%
 
ValueCountFrequency (%) 
1.82071125510.2%
 
1.82059657610.2%
 
1.80438232410.2%
 
1.77118933210.2%
 
1.76858055610.2%
 
1.76746678410.2%
 
1.76568770410.2%
 
1.76144814510.2%
 
1.75977098910.2%
 
1.74785041810.2%
 

oz5
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.2794120013713836e-10
Minimum-1.559006929397583
Maximum4.075531482696533
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:41.751119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.559006929
5-th percentile-1.070742303
Q1-0.7218126953
median-0.3281277269
Q30.4684445783
95-th percentile1.997178966
Maximum4.075531483
Range5.634538412
Interquartile range (IQR)1.190257274

Descriptive statistics

Standard deviation0.9999999996
Coefficient of variation (CV)-4387096317
Kurtosis1.496385304
Mean-2.279412001e-10
Median Absolute Deviation (MAD)0.5199762359
Skewness1.285194471
Sum-1.139706001e-07
Variance0.9999999993
2020-08-25T00:50:41.856957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.867294490310.2%
 
-0.413901239610.2%
 
0.516921997110.2%
 
-0.546221196710.2%
 
-0.776098549410.2%
 
-1.20076215310.2%
 
-1.26767802210.2%
 
1.97199094310.2%
 
1.37957191510.2%
 
-1.20746684110.2%
 
-0.100543953510.2%
 
-0.789399206610.2%
 
0.420091658810.2%
 
-0.664004325910.2%
 
-0.468920826910.2%
 
0.994835555610.2%
 
-1.0007537610.2%
 
-0.800153195910.2%
 
1.24708020710.2%
 
-0.481555432110.2%
 
0.455741882310.2%
 
-0.34197357310.2%
 
-1.09868824510.2%
 
1.16086626110.2%
 
-0.332698315410.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.55900692910.2%
 
-1.49530482310.2%
 
-1.41805684610.2%
 
-1.28058767310.2%
 
-1.26964247210.2%
 
-1.26952099810.2%
 
-1.26767802210.2%
 
-1.23608672610.2%
 
-1.2321573510.2%
 
-1.22572362410.2%
 
ValueCountFrequency (%) 
4.07553148310.2%
 
3.9596469410.2%
 
3.65391063710.2%
 
3.42490100910.2%
 
3.17286205310.2%
 
2.9754891410.2%
 
2.8947272310.2%
 
2.75235605210.2%
 
2.73679184910.2%
 
2.68723344810.2%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.972121220944928e-10
Minimum-1.685320258140564
Maximum1.6249237060546875
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:41.972065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.685320258
5-th percentile-1.548834127
Q1-0.869287774
median0.01500059571
Q30.9052449614
95-th percentile1.475484806
Maximum1.624923706
Range3.310243964
Interquartile range (IQR)1.774532735

Descriptive statistics

Standard deviation0.9999999984
Coefficient of variation (CV)-1254371290
Kurtosis-1.286844787
Mean-7.972121221e-10
Median Absolute Deviation (MAD)0.8886463642
Skewness-0.07847586835
Sum-3.98606061e-07
Variance0.9999999968
2020-08-25T00:50:42.078536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.631834089810.2%
 
-0.631193339810.2%
 
-0.952453732510.2%
 
1.22913014910.2%
 
1.29643154110.2%
 
-0.783514559310.2%
 
-0.0753565058110.2%
 
0.0713715702310.2%
 
-0.340002149310.2%
 
-0.833325862910.2%
 
-0.812820136510.2%
 
1.15885078910.2%
 
0.272135496110.2%
 
-1.57878947310.2%
 
1.18816709510.2%
 
1.4225459110.2%
 
-1.31908571710.2%
 
-0.31413289910.2%
 
-1.34885525710.2%
 
-1.60224914610.2%
 
-0.417652875210.2%
 
0.325385719510.2%
 
1.23702216110.2%
 
-0.798199176810.2%
 
-0.955039143610.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.68532025810.2%
 
-1.67263424410.2%
 
-1.67138361910.2%
 
-1.66332614410.2%
 
-1.66322982310.2%
 
-1.66219890110.2%
 
-1.6573972710.2%
 
-1.65739285910.2%
 
-1.65621411810.2%
 
-1.65450727910.2%
 
ValueCountFrequency (%) 
1.62492370610.2%
 
1.60988473910.2%
 
1.60403072810.2%
 
1.60340738310.2%
 
1.59097528510.2%
 
1.57429671310.2%
 
1.57339096110.2%
 
1.56784915910.2%
 
1.56345796610.2%
 
1.56104314310.2%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.428889274597168e-09
Minimum-1.739501714706421
Maximum1.7392500638961792
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:42.194517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.739501715
5-th percentile-1.568450004
Q1-0.8606422842
median0.01207866985
Q30.8538070172
95-th percentile1.541072178
Maximum1.739250064
Range3.478751779
Interquartile range (IQR)1.714449301

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)411710822.5
Kurtosis-1.190305638
Mean2.428889275e-09
Median Absolute Deviation (MAD)0.8556887479
Skewness-0.02488211445
Sum1.214444637e-06
Variance1.000000002
2020-08-25T00:50:42.292990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.509765088610.2%
 
0.590191960310.2%
 
0.83430987610.2%
 
1.12747192410.2%
 
1.66667997810.2%
 
0.697439491710.2%
 
1.35223138310.2%
 
0.727874040610.2%
 
1.18911731210.2%
 
1.62041723710.2%
 
0.937160670810.2%
 
1.0299761310.2%
 
-0.224695026910.2%
 
1.23285484310.2%
 
-1.19991385910.2%
 
-1.06319832810.2%
 
0.151460602910.2%
 
0.991562545310.2%
 
0.970078706710.2%
 
0.608751773810.2%
 
0.224108666210.2%
 
0.668325722210.2%
 
-1.71781158410.2%
 
-1.64838874310.2%
 
1.35415720910.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.73950171510.2%
 
-1.73908722410.2%
 
-1.72865927210.2%
 
-1.71798610710.2%
 
-1.71781158410.2%
 
-1.71166992210.2%
 
-1.71000075310.2%
 
-1.6770235310.2%
 
-1.65883028510.2%
 
-1.64905679210.2%
 
ValueCountFrequency (%) 
1.73925006410.2%
 
1.72964131810.2%
 
1.69983792310.2%
 
1.69789218910.2%
 
1.69624435910.2%
 
1.69519507910.2%
 
1.69197082510.2%
 
1.68346285810.2%
 
1.67047834410.2%
 
1.66667997810.2%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0884832590818405e-09
Minimum-1.8605743646621704
Maximum1.6176687479019165
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:42.401434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.860574365
5-th percentile-1.650012505
Q1-0.789412275
median0.05144590326
Q30.8508638293
95-th percentile1.494103491
Maximum1.617668748
Range3.478243113
Interquartile range (IQR)1.640276104

Descriptive statistics

Standard deviation0.9999999982
Coefficient of variation (CV)-918709580.4
Kurtosis-1.137472646
Mean-1.088483259e-09
Median Absolute Deviation (MAD)0.8278446775
Skewness-0.1553528473
Sum-5.442416295e-07
Variance0.9999999965
2020-08-25T00:50:42.507584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.21873807910.2%
 
0.00683094235110.2%
 
0.923338830510.2%
 
1.12405395510.2%
 
1.16080617910.2%
 
-0.464030146610.2%
 
1.274055610.2%
 
0.196615174410.2%
 
0.636070251510.2%
 
-1.36589324510.2%
 
0.529111564210.2%
 
-1.14910292610.2%
 
0.880216360110.2%
 
1.32098364810.2%
 
0.489891946310.2%
 
-1.26825833310.2%
 
-0.493462383710.2%
 
0.202722415310.2%
 
0.900733649710.2%
 
1.56119823510.2%
 
0.756941497310.2%
 
1.52648520510.2%
 
-1.76812541510.2%
 
0.0545266047110.2%
 
1.51014208810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.86057436510.2%
 
-1.8534861810.2%
 
-1.85148012610.2%
 
-1.85045254210.2%
 
-1.8426719910.2%
 
-1.83033382910.2%
 
-1.82989692710.2%
 
-1.80413985310.2%
 
-1.7815891510.2%
 
-1.77589237710.2%
 
ValueCountFrequency (%) 
1.61766874810.2%
 
1.58852112310.2%
 
1.58609497510.2%
 
1.58557903810.2%
 
1.58475482510.2%
 
1.56119823510.2%
 
1.56058669110.2%
 
1.5595737710.2%
 
1.55581021310.2%
 
1.55431866610.2%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.539025783538818e-11
Minimum-1.635672688484192
Maximum1.7711092233657837
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:42.625973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.635672688
5-th percentile-1.442635465
Q1-0.908704862
median-0.08834097162
Q30.8935285211
95-th percentile1.544143826
Maximum1.771109223
Range3.406781912
Interquartile range (IQR)1.802233383

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)2.825636379e+10
Kurtosis-1.304143864
Mean3.539025784e-11
Median Absolute Deviation (MAD)0.9109911025
Skewness0.08943870187
Sum1.769512892e-08
Variance0.9999999999
2020-08-25T00:50:42.729280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.654981374710.2%
 
-0.478665292310.2%
 
-0.428327083610.2%
 
-1.40570032610.2%
 
-1.02580678510.2%
 
0.932899177110.2%
 
0.891405761210.2%
 
0.284525185810.2%
 
-0.537395834910.2%
 
-0.974723577510.2%
 
-1.36972200910.2%
 
-0.873338282110.2%
 
-0.12139768910.2%
 
0.972362339510.2%
 
0.853810489210.2%
 
1.29040205510.2%
 
0.902684867410.2%
 
-0.167647659810.2%
 
-1.30922615510.2%
 
0.242841094710.2%
 
-1.11392068910.2%
 
-0.806963682210.2%
 
0.0439654737710.2%
 
-1.31101989710.2%
 
-1.19136714910.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.63567268810.2%
 
-1.63526964210.2%
 
-1.63429367510.2%
 
-1.61710834510.2%
 
-1.60493230810.2%
 
-1.59998464610.2%
 
-1.58753764610.2%
 
-1.5847178710.2%
 
-1.58377361310.2%
 
-1.57500040510.2%
 
ValueCountFrequency (%) 
1.77110922310.2%
 
1.75822174510.2%
 
1.73684227510.2%
 
1.71247339210.2%
 
1.70869982210.2%
 
1.6938592210.2%
 
1.69157230910.2%
 
1.69155931510.2%
 
1.66967725810.2%
 
1.66684794410.2%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5180786223397717e-10
Minimum-1.8096053600311282
Maximum1.6645196676254272
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:42.844203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.80960536
5-th percentile-1.648106283
Q1-0.8015163392
median0.06305923127
Q30.8291240931
95-th percentile1.536393189
Maximum1.664519668
Range3.474125028
Interquartile range (IQR)1.630640432

Descriptive statistics

Standard deviation0.9999999997
Coefficient of variation (CV)2213330230
Kurtosis-1.127871332
Mean4.518078622e-10
Median Absolute Deviation (MAD)0.8189929724
Skewness-0.115055152
Sum2.259039311e-07
Variance0.9999999995
2020-08-25T00:50:42.944768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.17968642710.2%
 
0.287279099210.2%
 
1.25424265910.2%
 
-0.667150795510.2%
 
1.12368190310.2%
 
-0.75617778310.2%
 
0.345864087310.2%
 
1.2994741210.2%
 
-0.113362692310.2%
 
-0.798259913910.2%
 
-1.55143606710.2%
 
-1.2448061710.2%
 
1.45694625410.2%
 
0.278000205810.2%
 
0.313928991610.2%
 
-0.655613243610.2%
 
0.832401931310.2%
 
-1.7467782510.2%
 
0.723977208110.2%
 
-0.0127839809310.2%
 
-0.00205342541410.2%
 
-1.11984634410.2%
 
-0.555275440210.2%
 
-1.57688891910.2%
 
-1.38157737310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.8096053610.2%
 
-1.79722762110.2%
 
-1.78678286110.2%
 
-1.7861994510.2%
 
-1.78159916410.2%
 
-1.7776091110.2%
 
-1.77003002210.2%
 
-1.75789308510.2%
 
-1.7467782510.2%
 
-1.74588882910.2%
 
ValueCountFrequency (%) 
1.66451966810.2%
 
1.65975105810.2%
 
1.65898358810.2%
 
1.65806794210.2%
 
1.64875650410.2%
 
1.64695739710.2%
 
1.63818204410.2%
 
1.62584054510.2%
 
1.62450814210.2%
 
1.61926400710.2%
 

target
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5529956043944824e-10
Minimum-2.369097948074341
Maximum3.547152042388916
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:50:43.058908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.369097948
5-th percentile-1.811126196
Q1-0.6547327042
median0.121041894
Q30.6275521368
95-th percentile1.424955368
Maximum3.547152042
Range5.91624999
Interquartile range (IQR)1.282284841

Descriptive statistics

Standard deviation0.9999999956
Coefficient of variation (CV)2814526408
Kurtosis0.3960349997
Mean3.552995604e-10
Median Absolute Deviation (MAD)0.6272260547
Skewness-0.002101925167
Sum1.776497802e-07
Variance0.9999999912
2020-08-25T00:50:43.168409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.263671129910.2%
 
1.35230100210.2%
 
0.39519128210.2%
 
-0.15682455910.2%
 
-0.219174519210.2%
 
0.916366219510.2%
 
0.689804434810.2%
 
-2.04828429210.2%
 
-0.454025059910.2%
 
0.478204429110.2%
 
-1.88438844710.2%
 
-2.23580861110.2%
 
-2.06393504110.2%
 
-1.81322288510.2%
 
1.164789210.2%
 
-2.07273769410.2%
 
-0.619050860410.2%
 
1.58939015910.2%
 
1.09645545510.2%
 
-0.224319294110.2%
 
0.0153444269710.2%
 
-0.279308199910.2%
 
0.22836652410.2%
 
0.112718589610.2%
 
-1.85241043610.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.36909794810.2%
 
-2.34450101910.2%
 
-2.32127833410.2%
 
-2.23580861110.2%
 
-2.18208599110.2%
 
-2.15547275510.2%
 
-2.15106582610.2%
 
-2.1472661510.2%
 
-2.07350468610.2%
 
-2.07273769410.2%
 
ValueCountFrequency (%) 
3.54715204210.2%
 
3.4496574410.2%
 
3.27369403810.2%
 
3.02633094810.2%
 
2.80264711410.2%
 
2.5692827710.2%
 
2.4872002610.2%
 
2.40483140910.2%
 
2.21362876910.2%
 
2.15241074610.2%
 

Interactions

2020-08-25T00:50:23.121792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:23.237239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:23.364523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:23.485907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:23.780051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:23.901042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.028819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.157745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.289648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.414420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.541729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.670405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.801458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:24.936450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:25.068789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:25.209514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:25.348302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:25.495308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:25.640730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:25.783645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:25.921743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.058244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.201831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.323812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.458241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.596891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.729172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.858669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:26.993079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:27.129949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:27.266319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:27.397246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:27.535238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:27.667987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:27.989431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:28.130829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:28.283364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:28.418994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:28.551606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:28.693547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:28.832505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:28.969923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:29.107937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:29.246929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:29.379382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:29.501992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:29.633565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:29.758042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:29.891948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.014505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.142570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.276363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.410089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.544465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.675990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.804857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:30.931555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:31.070584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:31.202339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:31.337952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:31.469141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:31.606006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:31.743976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:31.888573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:32.199000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:32.336888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:32.468690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:32.595980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:32.738729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:32.875116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.013370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.153586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.300776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.436759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.573675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.708273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.851173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:33.988800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:34.125838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:34.274400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:34.422449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:34.585086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:34.735090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:34.880459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.031476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.176846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.319725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.463775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.596922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.723146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.860683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:35.999149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:36.137937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:36.440664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:36.578388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:36.713404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:36.854878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:36.988746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:37.124832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:37.258943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:37.389906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:37.523978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:37.667462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:37.806225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:37.949291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:38.104240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:38.245919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:38.394131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:38.531883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:38.666459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:38.811407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:38.935440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.065928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.190297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.320975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.446271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.572106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.699709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.833670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:39.967223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:40.099593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:50:43.296090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:50:43.522850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:50:43.932399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:50:44.163725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:50:40.331524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:50:40.764891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
00.6796840.952877-0.775190-1.733060-0.6465890.0344331.041780-0.9591601.428423-1.138218-2.344501
1-1.179604-1.153223-0.8335580.499121-0.814107-1.2578630.590192-0.604118-0.8197320.5659320.318339
2-1.438627-1.058979-0.8679381.201597-1.138270-0.8197491.2013290.2267871.1503290.6680660.412499
3-0.994038-1.632690-1.160491-0.702969-0.895909-0.631834-1.169687-0.9345180.4322850.298222-0.143536
41.7336371.218325-0.346761-1.456528-0.165564-0.783515-1.137522-0.3991171.154339-1.4154550.028061
50.5148180.3341710.159119-0.1250200.365906-0.196987-0.6718311.354768-0.461568-0.415624-0.780270
6-1.526539-1.391847-1.312425-0.803206-0.9371460.450759-1.209131-1.5942871.585179-1.198747-0.129644
70.6662480.3873680.211212-0.0065320.208875-1.3966380.4697140.004992-0.897053-1.351189-1.253659
80.186038-0.148631-0.907602-1.060858-0.846474-1.1661451.195942-0.2468451.0483810.5277600.207132
90.9539231.4210110.455650-0.9963730.762266-0.8677800.788230-0.767805-1.378630-0.568255-0.844768

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
490-0.979647-1.449767-1.093835-1.148401-0.898184-0.2144921.442779-0.846577-0.9360260.236317-0.619051
4910.5247660.4539240.459566-0.0339520.388096-0.0849850.9314281.449754-0.861209-0.798260-1.068016
4920.3228541.2886430.209383-1.3854550.237856-0.036729-1.117916-1.1094110.889553-0.416984-2.052412
4930.5376460.5990590.2433510.0935650.072084-0.3367431.1178150.924267-1.4634261.638182-1.604441
494-0.528827-0.1133990.8964611.4056010.004000-1.3672901.337680-0.4934621.4778031.4569461.505719
495-0.3213380.480333-1.032417-0.957035-1.081581-1.6022491.670478-0.4061711.544095-1.008897-0.156825
496-0.822729-0.1185900.4147651.5559240.4132181.228889-0.174920-0.810374-0.9271001.5993591.515751
4970.3654981.0507121.7776541.1379051.275562-0.769166-0.7133100.3161790.140309-0.976387-0.844423
498-0.810828-1.528588-1.0939100.374872-0.6018280.4603950.2241090.694046-0.633195-1.1129300.439787
499-1.149479-1.571808-0.9646990.746660-0.9275040.4605940.344320-1.1324371.130537-1.4391230.108033